Construction of user portrait model of Internet medical platform based on text mining: A case study of autism diagnosis data

Corresponding author: Lvyanhua, Lvyanhua01@163.com

DOI: 10.12201/bmr.202312.00012

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  Abstract: Objective/Significance Construct Internet consultation user portraits to explore consultation topics, improve consultation service quality, reduce communication barriers between doctors and patients, and carry out targeted treatment in an online and offline manner. Methods: Python crawler was used to obtain the autism diagnosis data of a medical platform, and the combined model of LDA and TF-TFIDF was used to divide the data, and the user group classification was realized after dimensional reduction clustering. Finally, the characteristic sets of different user groups were calculated and output by Logistic regression model to construct the portrait. Results/Conclusion The inquiry content of users mainly focuses on several topics. The platform can optimize the inquiry filling template according to the typical characteristics of the subject content, enhance the accuracy of the disease description filling by users, and improve the inquiry efficiency and patient satisfaction.

  Key words: Internet medical platform, topic model, text analysis, user portrait

  Submit time: 11 December 2023

  Copyright: The copyright holder for this preprint is the author/funder, who has granted biomedRxiv a license to display the preprint in perpetuity.
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  2	2023-11-07	bmr.202312.00012V2	Download
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